The present disclosure relates generally to object and motion detection by a camera-equipped computer system. In particular, methods of compensating for camera movement while performing object recognition and gesture detection computer system are described.
As wearable technology such as the Google® Glass device continues to develop, so do new ways of interacting with such devices. One possible method of interaction is the use of gestures—motions made by the user with their fingers, hands, and/or arms—that allow the user to manipulate information displayed on a wearable device's display. Wearable devices typically position a display within the user's field of vision, allowing information to effectively be superimposed on whatever the user is viewing. By incorporating visual sensors into the wearable device, the user can make gestures within his or her field of view that appear, to the user, to manipulate the superimposed images. The accurate detection of such gestures requires the device to be able to distinguish between portions of the scene viewed by the visual sensor that are moving from those that are stationary. However, by their very nature, wearable devices are subject to frequent movement as the wearer goes about his or her daily routines. This presents a challenge to gesture detection; as the camera moves with the device movement, everything in the captured image appears to move, rendering the detection of a gesture apart from the rest of the scene problematic.
Known methods of gesture detection and object recognition, then, are not entirely satisfactory for the range of applications in which they are employed. For example, existing methods require the user to focus on holding still while making gestures or risk having a gesture either go unrecognized or be misinterpreted. In addition, employing conventional methods may result in the apparent motion of stationary objects resulting from camera movement being interpreted as a gesture.
Thus, there exists a need for methods that improve upon and advance the design of known methods of image capture for gesture detection and recognition. Examples of new and useful methods relevant to the needs existing in the field are discussed below.